Cryptocurrency mining selection system and method

ABSTRACT

A system and method of optimizing cryptographic mining yields includes analyzing, by a cryptocurrency mining selection system, data associated with factors of interest for one or more cryptocurrencies using machine learning algorithms. Data that is determined to be predictive of the future value of newly mined tokens is used to determine which tokens will have the highest and lowest future values. Based on the predicted value of tokens in the future and the current value of those tokens for each cryptocurrency, the system outputs one or more instructions to buy tokens in cryptocurrencies predicted to increase in value, to sell tokens in cryptocurrencies predicted to decrease in value, and to instruct associated cryptocurrency mining hardware to switch to generating new tokens in one or more selected cryptocurrencies to maximize yields.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/547,240, filed Aug. 21, 2019, which claims the benefit of U.S.Provisional No. 62/720,342, filed Aug. 21, 2018, the disclosures ofwhich are hereby incorporated herein by reference in their entireties.

TECHNICAL FIELD

Embodiments of the technology relate, in general, to optimizationalgorithms and technology, and in particular to systems and method foroptimized cryptocurrency mining (from hardware portfolio Selection tomining profit taking system), cryptocurrency staking, portfoliooptimization with traditional and crypto assets, and a cryptocurrencytrading system platform.

BACKGROUND

Cryptocurrency mining typically involves using specialized hardwaredesigned to execute complex hashing algorithms to find and validatehashes which become currency in a particular cryptocurrency network,such as BITCOIN. Cryptographic hashes are stored in blocks using adistributed ledger technology (DLT) or blockchain that is shared andsynchronized between multiple computer systems. The sharing andsynchronization of the blockchain allows the growing list of records tobe resistant to unauthorized changes.

The specialized hardware used in cryptocurrency mining can involve alarge number of high end graphics cards or customized processorsconfigured to process massive numbers of calculations in parallel tofind tokens. Depending on the current value of a selectedcryptocurrency, it may become uneconomical to mine for tokens in certaincryptocurrencies given the cost of the hardware, the cost of electricityto power and cool the hardware, and the amount of time it takes tosuccessfully find valid tokens, while other cryptocurrencies may becomemore economical given similar factors.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will become better understood with regard to thefollowing description, appended claims and accompanying drawingswherein:

FIG. 1 is a diagram of an example embodiment of a cryptocurrency miningselection system;

FIG. 2 is a diagram of example embodiments of subsystems of acryptocurrency mining selection system; and

FIG. 3 is a flowchart of example operations of an embodiment of acryptocurrency mining selection system.

DETAILED DESCRIPTION

The systems and methods disclosed herein are described in detail by wayof examples and with reference to the figures. It will be appreciatedthat modifications to disclosed and described examples, arrangements,configurations, components, elements, apparatuses, devices methods,systems, etc. can suitably be made and may be desired for a specificapplication. In this disclosure, any identification of specifictechniques, arrangements, etc. are either related to a specific examplepresented or are merely a general description of such a technique,arrangement, etc. Identifications of specific details or examples arenot intended to be, and should not be, construed as mandatory orlimiting unless specifically designated as such.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment,” or “in anembodiment” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures or characteristics may be combined in any suitablemanner in one or more embodiments.

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and operations of a cryptocurrency mining system.One or more examples of these non-limiting embodiments are illustratedin the accompanying drawings. Those of ordinary skill in the art willunderstand that systems and methods specifically described herein andillustrated in the accompanying drawings are non-limiting embodiments.The features illustrated or described in connection with onenon-limiting embodiment may be combined with the features of othernon-limiting embodiments. Such modifications and variations are intendedto be included within the scope of the present disclosure.

The examples discussed herein are examples only and are provided toassist in the explanation of the apparatuses, devices, systems andmethods described herein. None of the features or components shown inthe drawings or discussed below should be taken as mandatory for anyspecific implementation of any of these the apparatuses, devices,systems or methods unless specifically designated as mandatory. For easeof reading and clarity, certain components, modules, or methods may bedescribed solely in connection with a specific figure. Any failure tospecifically describe a combination or sub-combination of componentsshould not be understood as an indication that any combination orsub-combination is not possible. Also, for any methods described,regardless of whether the method is described in conjunction with a flowdiagram, it should be understood that unless otherwise specified orrequired by context, any explicit or implicit ordering of stepsperformed in the execution of a method does not imply that those stepsmust be performed in the order presented but instead may be performed ina different order or in parallel.

Described herein are example embodiments of computer-based systems andmethods for determining optimized cryptocurrency solutions. In variousembodiments, algorithms can be used to determine which cryptocurrenciesare yielding the greatest profit from mining. In various embodiments, asystem can automatically transition from one cryptocurrency to anothercryptocurrency based upon various economic factors. Embodiments of thesystem described herein can optimize cryptocurrency mining by focusingon mining coins or tokens to generate the highest yield or profit. Forexample, one cryptocurrency may have a reduced yield based upon demand,the number of miners, or other conditions, where switching to mining adifferent cryptocurrency may be advantageous. In various embodiments, asingle miner or bank of miners can be used to mine one or a plurality ofdifferent cryptocurrencies depending on various economic factors.

Embodiments of a cryptocurrency mining optimized computer system can runon any suitable computing system, such as a dedicated server, a usercomputer or server, multiple computers, a collection of networkedcomputers, a cloud-based computer system, a web-based computer system,or from a storage device, for example. One or multiple processing units,such as central processing units and/or graphics processing units, mayperform instructions stored in memory to execute the processes describedherein.

Embodiments of a cryptocurrency mining optimized computer system inaccordance with the present disclosure can be accessed via any suitabletechnique, such as a web-browser such as SAFARI, OPERA, GOOGLE CHROME,INTERNET EXPLORER, or the like executing on a client device. In someembodiments, the systems and methods described herein can be a web-basedapplication or a stand-alone executable. Additionally, in someembodiments, the systems and methods described herein can integrate withvarious types of cryptocurrency exchange systems, such as COINBASE, andthe like.

Any suitable client device can be used to access, or execute, thecryptocurrency mining optimized computing system, such as laptopcomputers, desktop computers, smart phones, tablet computers, gamingsystem, and the like. User interaction with the cryptocurrency miningoptimized computer system may take place in any of a variety ofoperational environments, such as a work setting or a home setting, withone or more users interacting with the system at a given time. Users canreceive real-time feedback, or near real-time feedback, and cansynchronize with one or more crypto exchange computer systems.

In accordance with the subject application, FIG. 1 illustrates anexample embodiment of a cryptocurrency mining selection system 100. Thesystem 100 includes a data acquisition subsystem 102, a data treatmentsubsystem 104, a selection subsystem 106, a prediction subsystem 108, anevaluation subsystem 110, a training subsystem 112, and a generation subsystem 114.

The data acquisition subsystem 102 can acquire data from distributedledger technology (DLT), or blockchains, including data public DLTs 120and private DLTs 122. DLTs 120, 122 can provide information such as thedegree of difficulty or cost to generate a token, hashrate, the numberof active wallets and so forth. The data treatment subsystem 104 cantake raw data and extract useful data that is converted into a suitableformat for use in the selection subsystem 106.

The selection subsystem 106 can determine which of the acquired data isuseful for determining the value of mining one or more cryptocurrencies.The selection subsystem 106 can receive input and process factors frommarkets or exchanges (cryptocurrencies, FIAT, economic and financialindicators, and the like), crowd, and network data among other suitablefactors. The prediction subsystem 108 can determine a predicted value ofone or more cryptocurrencies. The evaluation subsystem 110 can evaluatethe current value of mining one or more cryptocurrencies. The evaluationsubsystem 110 can periodically evaluate the performance of theinvestment portfolio, rebalancing it whenever necessary to correctperformance degradations in different time horizons. The trainingsubsystem 112 can generate models for weighting factors used to selectwhich cryptocurrencies are selected for mining by the system 100. Thegeneration subsystem 114 can output indicia related to the current andpredicted value of cryptocurrencies, for example indicia related to thehighest value token. The outputs can be directed to automated tradingsystems 130 such as advice or instructions for buying or sellingcryptocurrencies, and a robot advisory system 132 that directscryptocurrency mining hardware to mine one or several cryptocurrencies.

The system 100 can include processors executing algorithms such asneural networks, deep learning, machine learning and/or statisticalanalysis, to model, track the performance of a given cryptocurrencymarket, output a forecast for a given token, and manage a portfolio ofcryptocurrencies. Each model can be trained using specific inputs, knownhere as factors, and any kind of information as algorithm optimization,hyperparameters, loss function, likelihood, number of layers and thelike, and can be applied to supervised or unsupervised problems.

Referring to FIG. 2 , an example embodiment of subsystems of acryptocurrency mining selection system 200 is presented. The system 200includes the following subsystems:

-   -   A Recognition Subsystem 202 that allows users to identify        different ways of describing data; can be used to recognize        voices, faces, images, movements and color details making use of        structured, unstructured and geospatial data.    -   An Interpretation Subsystem 204 that extracts information from        any suitable source, in different formats, allowing raw data to        be compared and used in evaluation processes.    -   A Selection Subsystem 206 that filters data for a specific        period of time, tokens, analysis, and portfolios; establishes a        specific dataset to provide inputs of data to further procedures        and subsystems.    -   A Prediction Subsystem 208 that applies modeling techniques to        identify the patterns of a set of data; involves the use of        univariate and multivariate time series models, statistical        learning, and deep neural networks.    -   An Evaluation Subsystem 210 that validates the prediction model,        making use of sensitivity analysis of the parameters used in the        model, comparative accuracy and performance measurement        techniques and feedback of the estimates.    -   A Training Subsystem 212 that generates sufficient information        to validate the model used; can be performed periodically from        the cycle of new training and re-estimation dataset.    -   An Interpretation Subsystem 214 that outputs model forecasts        that can be used for investment decision making, critical        analysis, and opinion formation for multiple scenarios.

Referring to FIG. 3 , a flowchart of example operations 300 of anembodiment of a cryptocurrency mining selection system is presented.Processing starts at block 302 and proceeds to block 304. At block 304,factors of interest are generated for each type of cryptocurrency token.Factors of interest can include primary sources of information such asmarket input 306, crowd input 308, and network input 310. Market input306 can include the set of all the data available for public/privateconsultation, for example but not limited to, the information of price,volume, book value among others of the stock market, exchange, interestand futures around the world, economic data such as sentiment indexes,currency issuance, and inflation among other indexes, or consumersentiment information. Crowd input 308 can represent variables that areconsidered structured and unstructured and involve, for example but arenot limited to, information from social networks, websites, videos, newsvehicles and all others in text, voice, video, images. Network input 310can represent information that involves DLT technology and that can helpunderstand the value associated with the cryptocurrencies such as datarelated to the blockchain model used, degree of difficulty or cost togenerate the token, consensus form and so on can be used. Processingcontinues to block 312.

At block 312, data associated with each factor of interest for eachtoken is selected and treated. The choice of factors associated witheach token can include but is not limited to, crowd sentiment, networkdifficulty, network hashrate, main pools, number of active walletaddresses, correlation between token and stock market, analyzed andindexed, criticized and filtered in relation to the period of analysis,correlation and several other analysis statistics. Processing continuesto block 314.

At block 314, an information matrix is generated that represents boththe factors associated with each token, the data associated with eachfactor, and the relation between them. Processing continues to block316.

At block 316, the information matrix can be then modelled usingtechniques such as deep neural network and other learning algorithms,such as described above, to generate predictions and cryptocurrencymining valuations. Processing configured to block 318.

At block 318, the system outputs directives and analysis forcryptocurrency mining 320 and portfolio trading and management 322. Forexample, a directive can include an instruction to buy or sell assetsassociated with a cryptocurrency which can include trading the tokens.Analysis can include instructions or guidance to third parties aboutvarious cryptocurrencies. Processing ends at block 324.

In general, it will be apparent to one of ordinary skill in the art thatat least some of the embodiments described herein can be implemented inmany different embodiments of software, firmware, and/or hardware. Thesoftware and firmware code can be executed by a processor or any othersimilar computing device. The software code or specialized controlhardware that can be used to implement embodiments is not limiting. Forexample, embodiments described herein can be implemented in computersoftware using any suitable computer software language type, using, forexample, conventional or object-oriented techniques. Such software canbe stored on any type of suitable computer-readable medium or media,such as, for example, a magnetic or optical storage medium. Theoperation and behavior of the embodiments can be described withoutspecific reference to specific software code or specialized hardwarecomponents. The absence of such specific references is feasible, becauseit is clearly understood that artisans of ordinary skill would be ableto design software and control hardware to implement the embodimentsbased on the present description with no more than reasonable effort andwithout undue experimentation.

Moreover, the processes described herein can be executed by programmableequipment, such as computers or computer systems and/or processors.Software that can cause programmable equipment to execute processes canbe stored in any storage device, such as, for example, a computer system(nonvolatile) memory, an optical disk, magnetic tape, or magnetic disk.Furthermore, at least some of the processes can be programmed when thecomputer system is manufactured or stored on various types ofcomputer-readable media.

It can also be appreciated that certain portions of the processesdescribed herein can be performed using instructions stored on acomputer-readable medium or media that direct a computer system toperform the process steps. A computer-readable medium can include, forexample, memory devices such as diskettes, compact discs (CDs), digitalversatile discs (DVDs), optical disk drives, or hard disk drives. Acomputer-readable medium can also include memory storage that isphysical, virtual, permanent, temporary, semi-permanent, and/orsemi-temporary.

A “computer,” “computer system,” “host,” “server,” or “processor” canbe, for example and without limitation, a processor, microcomputer,minicomputer, server, mainframe, laptop, personal data assistant (PDA),wireless e-mail device, cellular phone, pager, processor, fax machine,scanner, or any other programmable device configured to transmit and/orreceive data over a network. Computer systems and computer-based devicesdisclosed herein can include memory for storing certain software modulesused in obtaining, processing, and communicating information. It can beappreciated that such memory can be internal or external with respect tooperation of the disclosed embodiments. The memory can also include anymeans for storing software, including a hard disk, an optical disk,floppy disk, ROM (read only memory), RAM (random access memory), PROM(programmable ROM), EEPROM (electrically erasable PROM) and/or othercomputer-readable media. Non-transitory computer-readable media, as usedherein, comprises all computer-readable media except for a transitory,propagating signal.

In various embodiments disclosed herein, a single component can bereplaced by multiple components and multiple components can be replacedby a single component to perform a given function or functions. Exceptwhere such substitution would not be operative, such substitution iswithin the intended scope of the embodiments. The computer systems cancomprise one or more processors in communication with memory (e.g., RAMor ROM) via one or more data buses. The data buses can carry electricalsignals between the processor(s) and the memory. The processor and thememory can comprise electrical circuits that conduct electrical current.Charge states of various components of the circuits, such as solid statetransistors of the processor(s) and/or memory circuit(s), can changeduring operation of the circuits.

Some of the figures can include a flow diagram. Although such figurescan include a particular logic flow, it can be appreciated that thelogic flow merely provides an exemplary implementation of the generalfunctionality. Further, the logic flow does not necessarily have to beexecuted in the order presented unless otherwise indicated. In addition,the logic flow can be implemented by a hardware element, a softwareelement executed by a computer, a firmware element embedded in hardware,or any combination thereof.

The foregoing description of embodiments and examples has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or limiting to the forms described. Numerous modificationsare possible in light of the above teachings. Some of thosemodifications have been discussed, and others will be understood bythose skilled in the art. The embodiments were chosen and described inorder to best illustrate principles of various embodiments as are suitedto particular uses contemplated. The scope is, of course, not limited tothe examples set forth herein, but can be employed in any number ofapplications and equivalent devices by those of ordinary skill in theart. Rather it is hereby intended the scope of the invention to bedefined by the claims appended hereto.

1-20. (canceled)
 21. A token mining selection system, comprising aserver, wherein the server comprises a processor and a memory, and aplurality of mining devices, wherein each of the plurality of miningdevices is configured to perform token mining activities; wherein theprocessor is configured to: (a) receive a market input dataset from oneor more market data sources, and determine a set of marketcharacteristics for one or more mineable tokens based on the marketinput dataset, wherein the set of market characteristics includes atleast a value; (b) receive a crowd input dataset from one or more crowddata sources, and determine a set of crowd characteristics for each ofthe one or more mineable tokens based on the crowd input dataset,wherein the set of crowd characteristics includes at least a sentiment;(c) monitor at least one public distributed ledger and at least oneprivate distributed ledger and produce a distributed ledger dataset; (d)determine a set of mining characteristics for each of the one or moreminable tokens, wherein the set of mining characteristics includes atleast a degree of difficulty, a hashrate, and a number of wallets; (e)create a predictive model for the one or more minable tokens based onthe set of market characteristics, the set of crowd characteristics, andthe set of mining characteristics for each of the one or more minabletokens, wherein the processor is configured to, when creating thepredictive model: (A) create the predictive model using a deep neuralnetwork based on the set of market characteristics, the set of crowdcharacteristics, and the set of mining characteristics; (B) periodicallyvalidate the predictive model based on a sensitivity analysis ofparameters used by the predictive model; (C) periodically perform newtraining of the predictive model based upon the periodic validation; and(g) provide an instruction for each of the plurality of mining devicesbased on analysis by the predictive model of the one or more mineabletokens, wherein the plurality of mining devices are configured to, inresponse to the instruction, perform token mining activities on adistributed ledger associated with one or more of the mineable tokens.